To address the data scarcity issue in Conversational question answering
(ConvQA), a dialog inpainting method, which utilizes documents to generate
ConvQA datasets, has been proposed. However, the original dialog inpainting
model is trained solely on the dialog reconstruction task, resulting in the
generation of questions with low contextual relevance due to insufficient
learning of question-answer alignment. To overcome this limitation, we propose
a novel framework called Dialogizer, which has the capability to automatically
generate ConvQA datasets with high contextual relevance from textual sources.
The framework incorporates two training tasks: question-answer matching (QAM)
and topic-aware dialog generation (TDG). Moreover, re-ranking is conducted
during the inference phase based on the contextual relevance of the generated
questions. Using our framework, we produce four ConvQA datasets by utilizing
documents from multiple domains as the primary source. Through automatic
evaluation using diverse metrics, as well as human evaluation, we validate that
our proposed framework exhibits the ability to generate datasets of higher
quality compared to the baseline dialog inpainting model.

为了解决 Conversational question answering (ConvQA) 中的数据稀缺问题，我们提出了一种名为 Dialogizer 的新型框架，能够从文本源自动生成具有高上下文相关性的 ConvQA 数据集。框架中结合了两种训练任务：question-answer matching (QAM) 和 topic-aware dialog generation (TDG)。在推理阶段，基于生成的问题的上下文相关性进行重排。通过使用多个领域的文档作为主要来源，使用我们的框架，我们生成了四个 ConvQA 数据集。通过多样化指标的自动评估以及人工评估，我们验证了我们提出的框架相较于基线对话修复模型具有更高质量的数据集生成能力。